Guilherme Pires Silva de Almeida

This study introduces AgroInsect, a smartphone-based system that combines on-device deep learning detection using the YOLOv11n model, deployed in the TensorFlow Lite (TFLite) format, with geostatistical mapping to support in-field pest monitoring in soybean and maize crops. The system detects four key pest species (Diabrotica speciosa, Dalbulus maidis, Diceraeus spp., and Spodoptera frugiperda) directly on the device, automatically extracts geolocation from image EXIF metadata, validates spatial consistency against farm boundaries using the Haversine formula, and synchronizes detections to a cloud database. Heatmaps and continuous incidence surfaces are generated via Ordinary Kriging (implemented in PyKrige), enabling visualization of spatial pest patterns. Field evaluations on a mid-range Android smartphone demonstrated strong detection performance, achieving 95.1% overall accuracy, F1-scores > 0.94 for all species, and a false detection rate of only 1.1%. Kriging validation under dense sampling yielded R² > 0.94, faithfully reproducing species-specific ecological distributions (localized for Diabrotica speciosa, diffuse for Dalbulus maidis.). The fully integrated, low-connectivity workflow proved robust in real field conditions, positioning AgroInsect as a scalable, practical tool for precision pest monitoring and faster IPM decision-making in resource-limited rural areas.

One of the most challenging aspects of agricultural pest control is accurate detection of insects in crops. Inadequate control measures for insect pests can seriously impact the production of corn and soybean plantations. In recent years, artificial intelligence (AI) algorithms have been extensively used for detecting insect pests in the field. In this line of research, this paper introduces a method to detect four key insect species that are predominant in Brazilian agriculture. Our model relies on computer vision techniques, including You Only Look Once (YOLO) and Detectron2, and adapts them to lightweight formats—TensorFlow Lite (TFLite) and Open Neural Network Exchange (ONNX)—for resource-constrained devices. Our method leverages two datasets: a comprehensive one and a smaller sample for comparison purposes. With this setup, the authors aimed at using these two datasets to evaluate the performance of the computer vision models and subsequently convert the best-performing models into TFLite and ONNX formats, facilitating their deployment on edge devices. The results are promising. Even in the worst-case scenario, where the ONNX model with the reduced dataset was compared to the YOLOv9-gelan model with the full dataset, the precision reached 87.3%, and the accuracy achieved was 95.0%.